Thermospheric Gravity Waves and Ionospheric Disturbances Triggered by Mountain Waves Over the Western US in January 2017
Bibliographic record
Abstract
Abstract We analyze an episode of strong mountain wave (MW) activity over the western US from 9 to 12 January 2017 using the HIgh Altitude mechanistic General Circulation Model. We find that medium‐scale MWs were generated by strong eastward flow over the Sierra Nevada and the Rocky Mountains. During this time, part of the stratospheric polar vortex jet extended from the western US to eastern Canada such that the MWs propagated into the lower mesosphere where they dissipated from westward vertical wind shear. This resulted in secondary gravity waves (GWs) that propagated into the lower thermosphere where tertiary GWs having concentric ring structures were created. With increasing altitude in the thermosphere, certain propagation directions were highlighted as a result of the dissipation induced by the tidal winds. At 260 km, we find eastward propagation during local morning over the northeastern US, equatorward propagation around local noon over the southern US, westward propagation during local afternoon over the northwestern US, and poleward propagation over Canada after local midnight. In addition, the model shows equatorward propagating larger‐scale GWs over Canada from remote sources around local noon. The simulated regional GW‐mean flow interaction patterns are consistent with multi‐step vertical coupling triggered by the MWs. The traveling ionospheric disturbances (TIDs) during the MW event are simulated with the ionospheric model SAMI3. The simulated GWs and TIDs are consistent with the medium‐to‐large‐scale TIDs observed over the continental US in GPS TEC data.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".